add all files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install Python deps
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app code
+COPY app.py .
+
+# Expose port used by uvicorn
+EXPOSE 7860
+
+# Run FastAPI server
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+app = FastAPI(
+    title="OpenAI-compatible Embedding API",
+    version="1.0.0",
+)
+
+# Load model from Hugging Face Hub
+MODEL_NAME = "BAAI/bge-small-en-v1.5"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModel.from_pretrained(MODEL_NAME)
+model.eval()
+
+class EmbeddingRequest(BaseModel):
+    input: list[str]
+    model: str
+
+@app.get("/")
+def root():
+    return {"message": "API is working"}
+
+    
+@app.post("/v1/embeddings")
+def create_embeddings(request: EmbeddingRequest):
+    with torch.no_grad():
+        tokens = tokenizer(request.input, return_tensors="pt", padding=True, truncation=True)
+        output = model(**tokens)
+        cls_embeddings = output.last_hidden_state[:, 0]
+        norm_embeddings = torch.nn.functional.normalize(cls_embeddings, p=2, dim=1)
+
+    data = [
+        {
+            "object": "embedding",
+            "embedding": e.tolist(),
+            "index": i
+        }
+        for i, e in enumerate(norm_embeddings)
+    ]
+
+    return {
+        "object": "list",
+        "data": data,
+        "model": request.model,
+        "usage": {
+            "prompt_tokens": sum(len(tokenizer.encode(x)) for x in request.input),
+            "total_tokens": sum(len(tokenizer.encode(x)) for x in request.input),
+        }
+    }

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch